This article provides a background introduction to pathway-based approaches for analyzing genome-wide association (GWA) studies. An example is shown to illustrate that many genes in a susceptibility pathway may show evidence of association, although not genome-wide significance, in any given GWA study.
A brief overview of published studies that use pathway approaches for interpreting data from GWA studies is given.
A summary and classification of the currently available pathway approaches is provided. The differences in their statistical approaches and analytical procedures are described.
A discussion of the challenges and pitfalls for using pathway approaches for analyzing GWA studies is then provided.
An outline of the future research directions that could further mine information from existing GWA study data sets is given. The extension of pathway approaches to next-generation sequencing data is also discussed.
Genome-wide association (GWA) studies have typically focused on the analysis of single markers, which often lacks the power to uncover the relatively small effect sizes conferred by most genetic variants. Recently, pathway-based approaches have been developed, which use prior biological knowledge on gene function to facilitate more powerful analysis of GWA study data sets. These approaches typically examine whether a group of related genes in the same functional pathway are jointly associated with a trait of interest. Here we review the development of pathway-based approaches for GWA studies, discuss their practical use and caveats, and suggest that pathway-based approaches may also be useful for future GWA studies with sequencing data.
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We thank D. C. Thomas (University of Southern California) for his helpful critiques which greatly improved the manuscript.
The authors declare no competing financial interests.
A strategy for assessing the probability of observing the value of a particular statistic. The probability is computed from a data set in which the data are randomly shuffled and the statistic is recomputed from the shuffled data many times and ultimately compared to the value of the statistic obtained with the non-shuffled data.
- Multi-marker test
A statistical method that measures the strength of association between a trait and multiple SNP markers.
- SNP ascertainment
Identification of SNPs that should be placed on a genotyping array to ensure representative coverage of the genome.
- Linkage disequilibrium
The non-random association of alleles at two or more closely linked loci.
- Genomic inflation
The presence of excess false-positive results, measured by quantifying the ratio of the median of the empirically observed distribution of the test statistic to the expected median.
- Type I error
The probability of a false-positive result from a statistical hypothesis test.
- Bonferroni correction
A multiple comparison adjustment approach that tests each individual hypothesis by dropping the threshold for declaring statistical significance by n-fold, when n hypotheses are being tested.
- False Discovery Rate
A multiple comparison adjustment approach to control the expected proportion of incorrectly rejected null hypotheses in a list of rejected hypotheses.
- Genotype imputation
A statistical method that predicts individual genotypes at ungenotyped markers from genotypes of other nearby markers, usually using the HapMap data as a reference.
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Wang, K., Li, M. & Hakonarson, H. Analysing biological pathways in genome-wide association studies. Nat Rev Genet 11, 843–854 (2010). https://doi.org/10.1038/nrg2884
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